par Mulki, Hala;Haddad, Hatem 
;Gridach, Mourad;Babaoglu, Ismail
Référence Proceedings of the conference - Association for Computational Linguistics. Meeting, page (664-669)
Publication Publié, 2017
;Gridach, Mourad;Babaoglu, IsmailRéférence Proceedings of the conference - Association for Computational Linguistics. Meeting, page (664-669)
Publication Publié, 2017
Article révisé par les pairs
| Résumé : | In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled “Sentiment analysis in Twitter”, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. Nevertheless, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work. |
